Currently, a wide variety of techniques and apparatus are used for diagnosing engines, including, but not limited to, rule based expert systems and fuzzy sets. Typically, the known techniques and apparatus operate to classify machine and/or component conditions into a two state situation, i.e., normal or abnormal.
However, it has been recognized that the identification of a machine or component fault is more properly categorized as a pattern recognition problem. Problems with rule based approaches include long execution time required for the system to look for all possible fault candidates then classify them according to likelihood.
Reference for instance, Schricker et al U.S. Pat. No. 5,646,341 issued Jul. 8, 1997, and U.S. Pat. No. 5,787,378 issued Jul. 28, 1998, both to Caterpillar Inc.
It is also known to diagnose an engine by comparing actual engine parameter values with modeled values. Reference, for instance, Brown Jr. et al U.S. Pat. No. 5,377,112 issued Dec. 27, 1994 to Caterpillar Inc. However, using computer based models alone it has been found to be difficult to provide accurate fault diagnosis when multiple fault conditions are present.
Wang et al U.S. Pat. No. 5,566,092 issued Oct. 15, 1996 to Caterpillar Inc. discloses a diagnostic system and method which integrates several different technologies, including neural networks, expert systems, physical models and fuzzy logic to provide on-line, real-time monitoring of machine components for possible failures. However, the disclosed method relies on substantial training and operator updating for accurate diagnosis.
Accordingly, the present invention is directed to overcoming one or more of the problems as set forth above.